{"id":146460,"date":"2006-12-01T00:00:00","date_gmt":"2006-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/cross-matching-multiple-spatial-observations-and-dealing-with-missing-data\/"},"modified":"2018-10-16T21:04:57","modified_gmt":"2018-10-17T04:04:57","slug":"cross-matching-multiple-spatial-observations-and-dealing-with-missing-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/cross-matching-multiple-spatial-observations-and-dealing-with-missing-data\/","title":{"rendered":"Cross-Matching Multiple Spatial Observations and Dealing with Missing Data"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Cross-match spatially clusters and organizes several astronomical point-source measurements from one or more surveys. Ideally, each object would be found in each survey. Unfortunately, even some stationary objects are missing in some observations; sometimes objects have a variable light flux and sometimes the seeing is worse. In most cases we are faced with a substantial number of differences in object detections between surveys and between observations taken at different times within a survey. Dealing with such missing observations is a difficult problem. The first step is to classify misses as ephemeral \u2013 when the object moved or simply disappeared, masked \u2013 when noise hid or corrupted the object observation, or edge \u2013 when the object was near the edge of the observational field. This classification and a spatial library to represent and manipulate observational footprints help construct a Match table recording both hits and misses. Transitive closure clusters friends-of-friends into object bundles. The bundle summary statistics are recorded in a Bundle table. This design is an evolution of the Sloan Digital Sky Survey cross-match design that compared overlapping observations taken at different times.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cross-match spatially clusters and organizes several astronomical point-source measurements from one or more surveys. Ideally, each object would be found in each survey. Unfortunately, even some stationary objects are missing in some observations; sometimes objects have a variable light flux and sometimes the seeing is worse. 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